To frame our analysis, it is important to clarify how misinformation is conceptualized across the reviewed studies. We found that authors do not always use consistent definitions or typologies of misinformation and disinformation. Through our review, we identified several categories of misinformation-related content that differ in both the nature of the falsehood and the intent underlying it. Drawing on prior frameworks, particularly the information disorder framework proposed by Wardle [7], we developed a working typology that provides a conceptual framework for interpreting the forms of misinformation and disinformation described in the reviewed literature. It was not used to assign all 100 studies to mutually exclusive categories or to generate category-specific frequencies. The typology classifies content and acts of dissemination; it does not classify unverifiable subjective experiences. Sender intent differentiates misguided, opportunistic, and malicious dissemination when intent can be supported; receiver interpretation or behavior is a downstream outcome and does not itself determine whether content is misinformation. Because intent is often difficult to infer from content alone, these categories should be treated as conceptual distinctions rather than definitive judgments about individual users. Figure 2 presents this typology along 2 conceptual dimensions: intent to deceive and potential harm.

This category encompasses factually false or misleading content shared without the intent to deceive or cause harm [7]. It may arise from satire, parody, reporting errors, humor, or sincere misunderstanding. Personal fears, concerns, opinions, and experiences are not misinformation unless they contain a fact-checkable false or misleading claim. This distinction is particularly important when interpreting vaccine-related social media discourse. For example, Stevens et al [26] examined anxiety, anger, sadness, and incivility in tweets about COVID-19 vaccine mandates but did not assess the factual accuracy of those tweets; therefore, the emotional expressions analyzed in that study should not themselves be classified as misinformation. By contrast, a claim such as “ice cream prevents COVID-19 infection,” if repeated as a joke or in a sincere mistake without deceptive intent, would constitute innocuous misinformation.

This category involves inaccurate or distorted information shared by individuals who sincerely believe it to be true. Rumors and unverified claims, especially common during crises when reliable information may be scarce, are central to this category [27]. For example, claims such as “mRNA alters a person’s DNA” or “the COVID-19 vaccine contains tissue from aborted fetuses” fall into this category when they are spread by individuals who mistakenly believe them to be accurate.

This category refers to false or misleading information deliberately disseminated to obtain personal, political, professional, or commercial advantage [28-30]. Unlike malicious disinformation, its primary objective is benefit seeking rather than the deliberate infliction of substantial harm, although harmful consequences may still occur [28]. Such content may exploit timely events, public attention, or existing concerns to attract engagement, influence perceptions, generate revenue, or promote interests. Examples include clickbait, exaggerated claims, and commercially motivated false or misleading health or product claims [29,30]. Its placement in the lower-harm quadrant indicates comparatively lower or more indirect potential harm than malicious disinformation; it does not imply that the consequences are necessarily harmless or minor.

This category refers to content that is knowingly and deliberately disseminated to mislead, distort public perceptions, or cause harm. It may include fabricated content, manipulated content, imposter content, and other misleading messages when there is evidence of deliberate deceptive intent [31]. The false claim linking fifth-generation (5G) mobile networks to the spread of COVID-19 is an example of a fabricated COVID-19 misinformation narrative with the potential to mislead the public and provoke fear [32]. It should be classified as malicious disinformation only when the available evidence supports knowing and deliberate dissemination; the falsity or potential harmfulness of the claim alone does not establish sender intent.

To address RQ2, we examined the online public health information contexts and thematic emphases represented in the reviewed studies. Instead of treating the categories as mutually exclusive groups of studies, we organize recurring findings into 2 parallel topic domains (general COVID-19 and public health/social measures and vaccination/vaccine uptake) and a set of cross-cutting analytical lenses. The domains and lenses can overlap conceptually; for example, amplification and cultural context may apply to both topic domains. The available review records do not support reliable per-theme study counts or an overlap matrix, so Figure 3 is presented as a conceptual map rather than a quantitative classification. This distinction is important for infodemiology because the observed concentration of topics and platforms shapes what kinds of misinformation become visible, which communities are studied, and what governance implications can be drawn.

This second topic domain concerns misinformation and related discourse about COVID-19 vaccination, vaccine confidence and hesitancy, and vaccine uptake. It examines not only false or misleading vaccine claims but also the discursive, behavioral, and political environments in which those claims circulate. The following paragraphs emphasize vaccine-related examples while illustrating analytical lenses that can also operate across the general COVID-19 and public health/social measures domain.

When applied to vaccination-related research, this analytical lens includes studies that identify, classify, and interpret vaccine-related misinformation and closely related forms of public discourse. A central concern in this line of work is to distinguish misinformation from other types of vaccine-related content, such as opinion, news, or general expressions of hesitancy, to better understand the structure of online vaccine discourse. For example, Weinzierl and Harabagiu [38] developed a graph link-prediction approach for the automated detection of COVID-19 vaccine misinformation. Ke et al [39] analyzed more than 646 million COVID-19-related tweets to investigate public health beliefs about mask-wearing and vaccination. Their study emphasized the role of “high-profile cues to action,” such as guidelines and statements from major institutions including the WHO and CDC, in shaping public beliefs and narratives. These studies show that vaccine discourse research is concerned not only with detecting false claims but also with mapping the broader informational and attitudinal landscape within which such claims circulate.

A more specific strand within this analytical lens focuses directly on the detection of vaccine-related misinformation and rumors. Hayawi et al [40] developed ML-based approaches to detect misinformation related to COVID-19 vaccines. Shang et al [41] similarly identified vaccine-related misleading posts on social media using community-contributed COVID-19 fact data (CCFD), while also addressing user privacy concerns. These studies demonstrate the importance of scalable detection methods for identifying misleading vaccine content and supporting timely public health responses.

A further analytical lens concerns the mechanisms through which vaccine-related misinformation is amplified on social media. These studies extend the analysis beyond the identification of misleading content to examine the relational, discursive, and political processes that enable misinformation to spread, gain attention, and reinforce hesitancy. For example, Guntuku et al [42] examined interaction patterns among users and showed how these dynamics can amplify both misinformation and negative sentiment, thereby intensifying vaccine hesitancy. Sauvayre et al [43] applied ML and NLP techniques to French-language tweets about vaccination and found that political considerations played a significant role in shaping vaccine-related discourse on X, formerly known as Twitter. Their findings suggest that vaccine misinformation does not circulate in a neutral informational environment; rather, it is often embedded in politically charged discussions that influence public interpretation and vulnerability to misleading claims. This line of research is important because it shifts attention from misinformation content alone to the social and political conditions that sustain its visibility and influence.

Another analytical lens examines the relationship between online vaccine discourse and real-world vaccination behavior. These studies are especially important because they connect social media analysis to measurable public health outcomes, thereby extending the relevance of online discourse beyond communication patterns alone. Bari et al [44] developed a real-time big data analytics framework using NLP-based sentiment analysis to track vaccine sentiment on Twitter. Their findings showed that negative sentiment increased after vaccine rollout, indicating that public discourse became more polarized over time. At the same time, they found that increases in positive Twitter sentiment were followed by increases in vaccination rates. Cross-correlation analyses across US regions further suggested that Twitter sentiment scores were associated with vaccination rates with an approximate one-week lag. These findings indicate that social media sentiment may function as an early signal of vaccine uptake and highlight the potential value of social media analytics for informing targeted communication and vaccination strategies.

The findings are organized around 2 major topic domains and multiple cross-cutting analytical lenses. This structure is intended to organize the findings; the topic domains and analytical lenses are not mutually exclusive. Cultural context, amplification, and discourse-behavior relationships may apply across both topic domains. Concentration around vaccines and public health/social measures is understandable given the urgency of the pandemic and the significance of vaccine uptake, but it narrows the empirical basis of the corpus. Importantly, the apparent scarcity of multimodal and behavioral evidence partly reflects the eligibility criteria: noncomputational behavioral studies and studies focused solely on nontextual image or video content were excluded. The identified gaps characterize the included corpus, not the entire COVID-19 misinformation literature. Future work should move beyond topic-specific and platform-specific snapshots toward comparative, cross-platform, multilingual, and multimodal analysis.

Data collection is a foundational stage in COVID-19 misinformation research because the choice of platform and acquisition strategy shapes both the scope of analysis and the kinds of claims that can be made. In the reviewed literature, most studies collected their own datasets from social media platforms, while a smaller number relied on publicly available datasets. Across these studies, data collection is structured by 2 core considerations: the selection of platform sources and the criteria used to retrieve relevant data. Together, these decisions determine what forms of discourse enter the dataset and what dimensions of misinformation can subsequently be observed. Figure 4 distinguishes data sources and communication modalities represented in the included studies from modalities that were absent or underrepresented in the corpus. The latter are shown to identify future research opportunities and should not be interpreted as evidence that corresponding studies were included in the reviewed sample.

The reviewed studies drew primarily on major text-based social media platforms. X, formerly known as Twitter, is the most frequently used source [45-49], followed by Weibo [50-53], Reddit [54-56], and Facebook [57-60]. These platforms are particularly prominent because they provide large volumes of user-generated textual data that are well-suited to misinformation analysis, discourse modeling, and computational classification.

X, formerly known as Twitter, is the most extensively used platform in the reviewed literature, largely because it offers high-volume, real-time, and relatively accessible discourse for analyzing rumor propagation, coordinated amplification, sentiment, and credibility. For example, Kirn and Hinders [61] analyzed more than 60 million tweets and used ridge count thresholding to identify coordinated tweetstorms and the communities most responsible for amplifying narratives across the platform. Abdelminaam et al [62] applied ML, DL, and NLP techniques to detect COVID-19 fake news across 4 benchmark datasets. Abe et al [63] used Twitter data to study rumors about toilet paper shortages during the pandemic. Similarly, Al-Rakhami and Al-Amri [64] developed a credibility assessment framework for COVID-19 tweets using 6 ML algorithms. These studies show that X/Twitter is especially useful for examining both large-scale diffusion dynamics and automated misinformation detection.

The dominance of X/Twitter also reflects differential platform data availability. During much of the review window, X/Twitter functioned as an easy-data environment because researchers could obtain large quantities of structured public data through its APIs, whereas platforms such as Facebook offered fewer public data and imposed stricter API limits [65]. Therefore, the number of X/Twitter studies should not be interpreted as a direct measure of that platform’s public health importance or misinformation prevalence.

Weibo is a major source of data for studies focusing on Chinese-language social media environments. Its importance lies in its ability to capture platform-specific discourse within the Chinese context, where rumor dynamics, language use, and interaction patterns may differ from those found on Western platforms. Lu et al [52] constructed a Weibo COVID-19 rumor dataset and proposed a few-shot learning-based fusion model integrating Bidirectional Encoder Representations from Transformers (BERT) embeddings, bidirectional gated recurrent units (Bi-GRUs), and meta-learning. Shi et al [53] used text, user, interaction, and emotion features to build an extreme gradient boosting (XGBoost) rumor detection model that achieved 91% accuracy. These studies suggest that Weibo is both an important regional data source and a useful setting for methodological experimentation in rumor detection.

Reddit occupies a distinct position because its subreddit structure allows researchers to study misinformation within topic-specific communities. Unlike more open broadcasting platforms, Reddit organizes discourse into semi-bounded spaces, making it well-suited to the analysis of community-level narratives, ideological reinforcement, and conspiracy-oriented discussion. Shahsavari et al [66] used Reddit and 4chan posts to identify emerging COVID-19 conspiracy theories through an ML-based graphical narrative discovery pipeline. Bhattacharyya [67] examined r/liberal and r/conservative to analyze the propagation and acceptance of COVID-related misinformation over time. Together, these studies show that Reddit is particularly valuable for understanding how misinformation becomes embedded in community dynamics and polarized discourse.

Facebook is less frequently represented than X/Twitter in the reviewed literature, but it remains an important platform because of its scale and influence on public communication. Research using Facebook data is particularly useful for examining interactions between misinformation, fact-checking, and user engagement. For instance, Xue et al [68] collected more than 12,000 fact-checking Facebook posts related to COVID-19 vaccines and over 122,000 associated comments. Using NLP and statistical analysis, they examined public attitudes, emotional patterns, and linguistic features in the vaccine fact-checking ecosystem. Their findings highlight the importance of third-party fact-checkers and suggest declining uncertainty about vaccines over time.

A smaller subset of studies also incorporates multimedia-sharing environments such as Instagram, LINE, and YouTube. These platforms expand the analytical scope of COVID-19 misinformation research by capturing visual, video-based, or message-chain communication that may not be fully represented in text-centered platforms. Zarei et al [69] published a large Instagram dataset and examined engagement patterns, hashtag use, and user behavior. Wang et al [70] studied rumor groups on LINE using a classification-based clustering approach, while Ginossar et al [71] analyzed YouTube videos shared in vaccine-related tweets and found a strong presence of conspiracy-related content. Although the reviewed literature remains predominantly focused on text-based data, these studies indicate that misinformation research is beginning to extend across multiple communication modalities.

Across the reviewed studies, social media data are typically acquired using 3 main criteria: keyword- or hashtag-based queries [72-75], temporal boundaries [76-79], and geographic filters [42,80-82]. These criteria are often used in combination to define the scope of a dataset, align collection procedures with a specific RQ, and facilitate the analysis of large-scale social media data. More importantly, they shape not only what data are collected but also what forms of misinformation become visible to analysis.

Keyword- and hashtag-based acquisition is common in studies focused on topical discourse, rumor detection, and vaccine-related conversation. Hagen et al [77] collected more than 1.3 million tweets using vaccine-related keywords and used network analysis and unsupervised ML to show that vaccine conversations on Twitter were highly polarized. Sharma et al [79] collected 40,000 tweets using COVID-related hashtags and then applied BERT and topic modeling techniques to identify false-information topics. These studies illustrate how keyword selection defines the topical boundaries of the dataset and directly affects the narratives that can be analyzed.

Temporal filtering is important in studies concerned with change over time. Benis et al [76] collected more than 2.7 million tweets across a broad time period to examine how influenza- and COVID-19-vaccination discussions evolved. By delimiting a specific temporal window, such studies can trace changes in attention, sentiment, and misinformation narratives across different stages of the pandemic.

Geographic filtering is especially useful when the goal is to analyze localized misinformation ecosystems or region-specific public discourse. Clamor et al [80] collected tweets from Metro Manila and nearby areas to examine COVID-19 misinformation in the Philippines. Using topic modeling and classification techniques, they identified multiple forms of user activity, including joking, awareness raising, criticism of government responses, and the sharing of preventive advice. This study illustrates how geographic constraints can help isolate context-specific discourse and reveal how misinformation is embedded in local communicative practices.

Overall, data acquisition criteria are not neutral procedural choices. They define the scope of the dataset, shape the patterns that become visible to analysis, and constrain the conclusions that can be drawn. Keyword selection determines topical inclusion, temporal boundaries capture discursive change, and geographic filters enable contextual specificity. These criteria form a key part of the methodological and interpretive boundaries of COVID-19 misinformation research.

Once data have been collected and transformed into analyzable forms, researchers use NLP and ML methods to address a range of analytical objectives. Across the reviewed literature, these objectives cluster around 4 broad areas: topic modeling and thematic analysis [93,94], sentiment and emotion analysis [95-97], false information detection and classification, including misinformation, disinformation, and fake news detection [98-102], and multilingual or cross-lingual analysis [103-106]. These approaches are not mutually exclusive; rather, they often overlap within the same study. Together, they show that COVID-19 misinformation research spans exploratory, interpretive, predictive, and comparative tasks. Figure 6 presents a high-level taxonomy of the major modeling and analysis approaches identified in the reviewed studies.

Topic modeling and thematic analysis are commonly used to identify recurring narratives, discursive patterns, and user communities associated with COVID-19 misinformation. These approaches are especially useful when the goal is to uncover the substantive structure of discourse rather than merely classify individual posts. Kalantari et al [107] combined topic identification, network analysis, and sentiment analysis to study misinformation-related Twitter discourse and user behavior. Velásquez et al [108] used Latent Dirichlet Allocation (LDA)-based topic modeling across multiple platforms to examine how online hate communities incorporated COVID-19 into malicious narratives and showed how platform moderation policies could influence the spread of such content. These studies demonstrate the value of topic modeling and thematic analysis for mapping discursive structure, tracing narrative evolution, and linking misinformation to broader community dynamics.

A second major line of work focuses on affective expression in social media discourse. Sentiment and emotion analysis are used to examine how fear, distrust, support, skepticism, and related emotional responses emerge in discussions surrounding vaccines, health policies, and misinformation. Prabagar et al [95] applied a multimodule AI framework to analyze emotional shifts in approximately 400,000 vaccine-related posts from Twitter and Reddit. Thakur [109] used the Valence Aware Dictionary and Sentiment Reasoner (VADER) to classify positive and negative sentiment in a large Twitter dataset, while Satu et al [83] used an ML classification model and other models to identify the most frequent topics and their associated sentiment categories across 6 datasets. These studies show that misinformation is not only informational in nature but also affective, and that sentiment-based methods can help capture the emotional conditions under which misinformation gains visibility and influence.

A substantial portion of the reviewed literature is devoted to the automated detection and classification of false information, including misinformation, disinformation, rumors, and fake news. Although the terminology varies across studies, the underlying methodological objective is similar: to distinguish misleading from reliable content at scale using textual, structural, network-based, or hybrid features. Ajao et al [110] tested multiple ML and DL models for detecting pandemic-related misinformation and found that support vector machine (SVM) outperformed RNN in their setting. Fedoruk et al [111] proposed the Plebeian algorithm, which combines sentiment analysis and postpopularity with a democratic moderation mechanism, thereby linking detection to platform governance. Gupta and Potika [112] examined the dissemination of fake versus real news through social network analysis and community detection, while Qureshi et al [113] found that hybrid features outperformed individual network or user features in source-based fake news detection. Other studies focus more directly on performance optimization. A total of 3 studies [114-116] compared a wide range of ML, DL, and transformer-based approaches to improve classification accuracy. Iwendi et al [117] demonstrated the value of richer feature spaces and domain-specific embeddings, while Nassif et al [118] showed the importance of language-specific contextual embedding models in Arabic fake news detection. Taken together, these studies reflect a strong technical orientation toward automating authenticity assessment. At the same time, they also suggest that classification performance alone is insufficient to address the broader sociotechnical challenges of misinformation, particularly when interpretability, governance, and real-world deployment are taken into account.

The multilingual nature of social media substantially increases the complexity of COVID-19 misinformation research. Because misleading narratives circulate across linguistic communities, approaches developed for English alone are insufficient for capturing the global scope of the infodemic. The reviewed literature includes work in Arabic [86,118,119], Chinese [51,52], Japanese [73], Sinhala [103], Persian [120], and other languages, highlighting the need for methods that are robust beyond English-centric settings. For example, Caldera et al [103] used Sinhala tweets to identify hate-speech trends and applied time-series analysis to predict commenting behavior. Ashraf et al [121] proposed an ML-based rumor detection model for English and Arabic COVID-19 tweets using TF-IDF features and multiple classifiers. In a related study, Almars et al [122] introduced a hybrid attention neural network combining CNN and Bi-LSTM to detect and interpret rumors in Arabic tweets. These studies show that multilingual and cross-lingual analysis requires more than simple language transfer; it often demands tailored feature design, annotation strategies, and model architectures. More broadly, this strand of the literature underscores that methodological robustness in misinformation research depends on sensitivity to linguistic and cultural variation. The English-language eligibility criterion applied to article publication, not to dataset language. The languages named here are illustrative examples from the subsection, not a complete census of the primary dataset languages across all 100 studies; the retained records do not support a reliable study-level language distribution.

Overall, the reviewed literature demonstrates the value of computational methods for processing large-scale online misinformation data. However, it also reveals a detection-centered orientation. Most studies use computational models to identify, classify, cluster, or describe misinformation-related content, while fewer studies connect these outputs to downstream governance tasks such as moderation, warning design, fact-checking workflows, public communication strategies, or intervention evaluation. This creates a gap between information processing and information governance. For online information research, the key challenge is not only whether misinformation can be detected accurately but also whether computational outputs can support explainable, actionable, and context-sensitive responses in real-world information environments. A related limitation is the insufficiently visible role of human-in-the-loop interpretation. Still, several included studies demonstrate ways to integrate human judgment with computational analysis. Dhungana Sainju et al [45] combined supervised ML with independent human coding and qualitative content analysis; Roe et al [74] paired automated sentiment analysis with questionnaire data; and Hagen et al [77] integrated network analysis and ML with manual inductive coding of community discourse. Other studies manually labeled Arabic tweets using categories derived from the Arabic Infectious Disease Ontology [86], evaluated explainable model outputs in a human-participant experiment [116], or developed detailed annotation guidelines for fine-grained, multilabel misinformation coding and assessed interannotator reliability using a second annotator on a 1500-tweet subsample [119]. These examples show that human expertise can contribute during annotation, validation, interpretation, and triangulation rather than only at the downstream governance stage. However, because human involvement was not retained as a separate study-level extraction variable, the review does not estimate its prevalence across the full corpus.